GE461 Spring 2025

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start [2025/02/22 20:28]
ge461 [Week 5 (Feb 24, Feb 27)] 		start [2025/04/24 07:07] (current)
ge461 [Week 15 (May 5, May 8)]
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 ** Data collection; storage; querying; SQL, NoSQL; cloud; distributed storage and computing. ** [Körpeoğlu] \\ ** Data collection; storage; querying; SQL, NoSQL; cloud; distributed storage and computing. ** [Körpeoğlu] \\
 Topic Details: RDMBs, SQL; SQLite, Pandas; NoSQL; MapReduce and Hadoop; Spark.\\ Topic Details: RDMBs, SQL; SQLite, Pandas; NoSQL; MapReduce and Hadoop; Spark.\\
-Slides and Additional Material:{{:slides.pdf | Slides.pdf}}\\+Slides and Additional Material:{{:slides.pdf | data_storage_and_access.pdf}}\\
 Project/Exercise-Problem-Set/Homework: None this week.\\ Project/Exercise-Problem-Set/Homework: None this week.\\
 References:  References: 
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 **  Dimensionality reduction; visualization.** [Aksoy] \\ **  Dimensionality reduction; visualization.** [Aksoy] \\
 Topic Details: Feature reduction, feature selection, high-dimensional data visualization.\\ Topic Details: Feature reduction, feature selection, high-dimensional data visualization.\\
-Slides and Additional Material:\\ +Slides and Additional Material: {{ :ge461_dimensionality.pdf |Dimensionality slides}}, {{ :knaw_t-sne_talk.pptx |t-SNE slides}}\\ 
-Project/Exercise-Problem-Set/Homework:\\+Project/Exercise-Problem-Set/Homework: [{{ :ge461_project_dimensionality.pdf |Project}} ({{ :fashion_mnist.zip |data}})]  (due 23:59 on April 7, 2025)\\
 References: [[https://www.mathworks.com/help/stats/dimensionality-reduction.html|Matlab: dimensionality reduction]], [[https://scikit-learn.org/stable/modules/decomposition.html|Scikit-learn: decomposition]], [[https://scikit-learn.org/stable/auto_examples/index.html#decomposition|Scikit-learn: decomposition examples]], [[https://scikit-learn.org/stable/modules/manifold.html|Scikit-learn: manifold learning]], [[https://www.mathworks.com/discovery/data-visualization.html|Matlab: data visualization]],  References: [[https://www.mathworks.com/help/stats/dimensionality-reduction.html|Matlab: dimensionality reduction]], [[https://scikit-learn.org/stable/modules/decomposition.html|Scikit-learn: decomposition]], [[https://scikit-learn.org/stable/auto_examples/index.html#decomposition|Scikit-learn: decomposition examples]], [[https://scikit-learn.org/stable/modules/manifold.html|Scikit-learn: manifold learning]], [[https://www.mathworks.com/discovery/data-visualization.html|Matlab: data visualization]], 
 [[https://matplotlib.org/|Matplotlib: data visualization]], [[https://lvdmaaten.github.io/tsne/|t-SNE]]\\ [[https://matplotlib.org/|Matplotlib: data visualization]], [[https://lvdmaaten.github.io/tsne/|t-SNE]]\\
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 ** Unsupervised learning, clustering.  ** [Aksoy] \\ ** Unsupervised learning, clustering.  ** [Aksoy] \\
 Topic Details: K-means clustering, mixture models, hierarchical clustering.\\ Topic Details: K-means clustering, mixture models, hierarchical clustering.\\
-Slides and Additional Material:\\+Slides and Additional Material: {{ :ge461_clustering.pdf |Clustering slides}}\\
 Project/Exercise-Problem-Set/Homework:  \\ Project/Exercise-Problem-Set/Homework:  \\
 References: [[https://www.mathworks.com/help/stats/cluster-analysis.html|Matlab: cluster analysis]], [[https://scikit-learn.org/stable/modules/clustering.html|Scikit-learn: clustering]], [[https://scikit-learn.org/stable/auto_examples/index.html#clustering|Scikit-learn: clustering examples]]\\ References: [[https://www.mathworks.com/help/stats/cluster-analysis.html|Matlab: cluster analysis]], [[https://scikit-learn.org/stable/modules/clustering.html|Scikit-learn: clustering]], [[https://scikit-learn.org/stable/auto_examples/index.html#clustering|Scikit-learn: clustering examples]]\\
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 ** Machine learning; supervised learning; classifiers; deep learning. ** [Dibeklioğlu]\\ ** Machine learning; supervised learning; classifiers; deep learning. ** [Dibeklioğlu]\\
 Topic Details: Bayesian decision theory, linear discriminants, introduction to neural networks, support vector machines, decision trees.\\ Topic Details: Bayesian decision theory, linear discriminants, introduction to neural networks, support vector machines, decision trees.\\
-Slides and Additional Material:\\+Slides and Additional Material:  {{ :ge461_supervisedlearning_part1_2025s.pdf |Supervised Learning Part-1}},  {{ :ge461_supervisedlearning_part2_2025s.pdf |Supervised Learning Part-2}}\\
 Project/Exercise-Problem-Set/Homework: \\ Project/Exercise-Problem-Set/Homework: \\
 References: \\ References: \\
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 ** Machine learning; supervised learning; classifiers; deep learning.** [Dibeklioğlu] \\ ** Machine learning; supervised learning; classifiers; deep learning.** [Dibeklioğlu] \\
 Topic Details: Activation functions, convolutional neural networks, recurrent architectures.\\ Topic Details: Activation functions, convolutional neural networks, recurrent architectures.\\
-Slides and Additional Material:\\ +Slides and Additional Material:{{ :ge461_deep_learning_2025s.pdf | Deep Learning}}\\ 
-Project/Exercise-Problem-Set/Homework:\\+Project/Exercise-Problem-Set/Homework:[{{ :GE461_project_supervised_learning_2025s.pdf |Project Description}}, {{ :data_supervised_learning_project.zip |Data}}] (due 23:55 on April 27, 2025)\\
 References: \\ References: \\
 Events: \\ Events: \\
 +
  
 ==== Week 13 (Apr 21, Apr 24) ====  ==== Week 13 (Apr 21, Apr 24) ==== 
 ** Machine learning in healthcare. ** [Çukur] \\ ** Machine learning in healthcare. ** [Çukur] \\
 Topic Details: Healthcare analytics: diagnostics, medical imaging, in-patient care, hospital management, risk analytics, wearables. Deep learning architectures for medical applications; \\ Topic Details: Healthcare analytics: diagnostics, medical imaging, in-patient care, hospital management, risk analytics, wearables. Deep learning architectures for medical applications; \\
-Slides and Additional Material:\\ +Slides and Additional Material: {{ ::ge461_ml_in_healthcare.pdf |}} \\ 
-Project/Exercise-Problem-Set/Homework:\\+Project/Exercise-Problem-Set/Homework: {{ ::ge461_pw13_description.pdf |}}; {{ ::ge461_pw13_data.zip |}} (due date: 11 May 2025, 17:00)\\
 References: Hastie, Tibshirani and Friedman, The Elements of Statistical Learning, Ch. 11 and 14; Mead, Analog VLSI and Neural Systems, Ch. 4; Bishop, Pattern Recognition and Machine Learning, Ch. 5\\ References: Hastie, Tibshirani and Friedman, The Elements of Statistical Learning, Ch. 11 and 14; Mead, Analog VLSI and Neural Systems, Ch. 4; Bishop, Pattern Recognition and Machine Learning, Ch. 5\\
 Events: National Sovereignty and Children's Day (Apr 23)\\ Events: National Sovereignty and Children's Day (Apr 23)\\
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 ** Data mining; online data stream classification; applications.**  [Can] \\ ** Data mining; online data stream classification; applications.**  [Can] \\
 Topic Details: Concept drift, ensemble-based classification, text mining. \\ Topic Details: Concept drift, ensemble-based classification, text mining. \\
-Slides and Additional Material:\\+Slides and Additional Material:  {{ :ge461_datastreamminingspring25.pdf |}}\\ 
 +Project Tentative Days: Announcement **April 28** or earlier, Due date: **May 18, 23:59.** \\
 Project/Exercise-Problem-Set/Homework:\\ Project/Exercise-Problem-Set/Homework:\\
 References:  \\ References:  \\
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 ** Reinforcement learning; applications.  ** [Tekin] \\ ** Reinforcement learning; applications.  ** [Tekin] \\
 Topic Details: Applications of Reinforcement Learning, Markov Decision Processes, Value Iteration, Q Learning\\ Topic Details: Applications of Reinforcement Learning, Markov Decision Processes, Value Iteration, Q Learning\\
-Slides and Additional Material:\\+Slides and Additional Material:{{ :ge461_reinforcementlearning.pdf |}} \\
 Project/Exercise-Problem-Set/Homework: \\ Project/Exercise-Problem-Set/Homework: \\
 References: \\ References: \\
start.1740256126.txt.gz · Last modified: 2025/02/22 20:28 by ge461

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